Fraud Detection Case Study

Overview

This project focuses on building a robust model for detecting fraudulent transactions using machine learning techniques. The implementation leverages exploratory data analysis (EDA), feature engineering, and various predictive modeling techniques to identify and mitigate financial fraud.

Objectives

• Perform exploratory data analysis to understand the characteristics of fraudulent transactions.
• Apply data preprocessing techniques to clean and prepare the dataset for modeling.
• Engineer meaningful features to improve predictive performance.
• Train and evaluate multiple machine learning models to detect fraud effectively.
• Select the best-performing model based on evaluation metrics.

Dataset

The dataset used contains historical transaction data labeled as fraudulent or legitimate. Key attributes include transaction amount, time, user details, and transaction type.

Methodology

1. Data Exploration

• Understanding data distribution.
• Identifying patterns and anomalies indicative of fraud.

2. Data Preprocessing

• Handling missing values.
• Dealing with class imbalance using techniques such as undersampling or oversampling.

3. Feature Engineering

• Creating derived features that enhance the ability to detect fraud.
• Scaling and normalizing data for improved model performance.

4. Modeling

• Implementing machine learning models such as Logistic Regression, Random Forest, and XGBoost.
• Evaluating models using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.

5. Model Selection and Evaluation

• Comparing performance across different models.
• Selecting the best-performing model based on metrics suited for imbalanced datasets.

Usage

• Clone this repository.
• Install necessary libraries using pip install -r requirements.txt.
• Run the Jupyter notebook Fraud_Detection_Case_Study_Code.ipynb to reproduce the analysis.

Dependencies

• Python 3.x
• Pandas
• NumPy
• Scikit-learn
• XGBoost
• Matplotlib
• Seaborn

Results

The best-performing model achieves a high level of accuracy and recall, significantly reducing false negatives and effectively identifying fraudulent transactions.

Future Work

• Explore additional advanced modeling techniques like neural networks.
• Implement real-time fraud detection solutions.
• Continuously update models with new transaction data to maintain accuracy.

License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.